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Publication Metadata only 3D articulated shape segmentation using motion information(Institute of Electrical and Electronics Engineers (IEEE), 2010) Department of Computer Engineering; N/A; Yemez, Yücel; Kalafatlar, Emre; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 107907; N/AWe present a method for segmentation of articulated 3D shapes by incorporating the motion information obtained from time-varying models. We assume that the articulated shape is given in the form of a mesh sequence with fixed connectivity so that the inter-frame vertex correspondences, hence the vertex movements, are known a priori. We use different postures of an articulated shape in multiple frames to constitute an affinity matrix which encodes both temporal and spatial similarities between surface points. The shape is then decomposed into segments in spectral domain based on the affinity matrix using a standard K-means clustering algorithm. The performance of the proposed segmentation method is demonstrated on the mesh sequence of a human actor.Publication Open Access 3D face recognition by projection based methods(Society of Photo-optical Instrumentation Engineers (SPIE), 2006) Dutaǧaci, Helin; Sankur, Bülent; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of EngineeringIn this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.Publication Open Access 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients(Public Library of Science, 2019) Dinçer, Cansu; Kaya, Tuğba; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; 26605; 8745Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways, revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between each group and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.Publication Metadata only A survey of energy efficiency in SDN: Software-based methods and optimization models(Elsevier, 2019) N/A; N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 113507Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modem networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.Publication Metadata only Affect burst detection using multi-modal cues(IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/ARecently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.Publication Metadata only Affect-expressive hand gestures synthesis and animation(IEEE, 2015) Department of Computer Engineering; N/A; Department of Computer Engineering; Erzin, Engin; Bozkurt, Elif; Yemez, Yücel; Faculty Member; PhD Student; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 34503; N/A; 107907Speech and hand gestures form a composite communicative signal that boosts the naturalness and affectiveness of the communication. We present a multimodal framework for joint analysis of continuous affect, speech prosody and hand gestures towards automatic synthesis of realistic hand gestures from spontaneous speech using the hidden semi-Markov models (HSMMs). To the best of our knowledge, this is the first attempt for synthesizing hand gestures using continuous dimensional affect space, i.e., activation, valence, and dominance. We model relationships between acoustic features describing speech prosody and hand gestures with and without using the continuous affect information in speaker independent configurations and evaluate the multimodal analysis framework by generating hand gesture animations, also via objective evaluations. Our experimental studies are promising, conveying the role of affect for modeling the dynamics of speech-gesture relationship. © 2015 IEEE.Publication Metadata only AffectON: Incorporating affect into dialog generation(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Bucinca, Zana; Department of Computer Engineering; Yemez, Yücel; Erzin, Engin; Sezgin, Tevfik Metin; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of EngineeringDue to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduce AffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. AffectON is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.Publication Open Access AffectON: incorporating affect into dialog generation(Institute of Electrical and Electronics Engineers (IEEE), 2020) Bucinca, Zana; Department of Computer Engineering; Yemez, Yücel; Erzin, Engin; Sezgin, Tevfik Metin; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; College of Engineering; 107907; 34503; 18632Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g. How are you ?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this paper, we introduce AffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. AffectON is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.Publication Metadata only An analytical framework for self-organizing peer-to-peer anti-entropy algorithms(Elsevier, 2010) N/A; Department of Computer Engineering; Department of Mathematics; Department of Mathematics; Department of Mathematics; Özkasap, Öznur; Çağlar, Mine; Yazıcı, Emine Şule; Küçükçifçi, Selda; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Mathematics; College of Engineering; College of Sciences; College of Sciences; College of Sciences; 113507; 105131; 27432; 105252An analytical framework is developed for establishing exact performance measures for peer-to-peer (P2P) anti-entropy paradigms used in biologically inspired epidemic data dissemination. Major benefits of these paradigms are that they are fully distributed, self-organizing, utilize local data only via pair-wise interactions, and provide eventual consistency, reliability and scalability. We derive exact expressions for infection probabilities through elaborated counting techniques on a digraph. Considering the first passage times of a Markov chain based on these probabilities, we find the expected message delay experienced by each peer and its overall mean as a function of initial number of infectious peers. Further delay and overhead analysis is given through simulations and the analytical framework. The number of contacted peers at each round of the anti-entropy approach is an important parameter for both delay and overhead. These exact performance measures and theoretical results would be beneficial when utilizing the models in several P2P distributed system and network services Such as replicated servers, multicast protocols, loss recovery, failure detection and group membership management.Publication Metadata only Analysis and network representation of hotspots in protein interfaces using minimum cut trees(Wiley, 2010) Department of Chemical and Biological Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; Tunçbağ, Nurcan; Salman, Fatma Sibel; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; 245513; 178838; 26605; 8745We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph-based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge-based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible view of critical residues and facilitates the inspection of their organization. We observed, on a nonredundant data set of 38 protein complexes with experimental hotspots that the highest degree node in the mincut tree usually corresponds to an experimental hotspot. Further, hotspots are found in a few paths in the mincut tree. In addition, we examine the organization of hotspots (hot regions) using an iterative clustering algorithm on two different case studies. We find that distinct hot regions are located on specific sites of the mincut tree and some critical residues hold these clusters together. Clustering of the interface residues provides information about the relation of hot regions with each other. Our new approach is useful at the molecular level for both identification of critical paths in the protein interfaces and extraction of hot regions by clustering of the interface residues.